The present invention relates to a ECG (Electrocardiograph) device with Impulse and Channel Switching ADC Noise filter and an Error corrector for Derived Leads. The present invention more particularly relates to implementation of a Burst Sampling technique for removing Impulsive and Channel Switching ADC Noise from the ECG signal and an interpolation algorithm for correction of errors in Derived ECG Leads caused by sequential sampling of directly measured ECG leads. This invention in particular relates to a medical device for measuring, filtering, monitoring and recording ECG signals. It also relates to extracting some of the vital parameters using analysis functions built into firmware/software.
The expressions used in foregoing paragraphs including the background are explained below with reference to FIG. 1.
Einthoven Leads: Leads I, II and III are called Einthoven leads and represent the electrical potential difference between a pair of electrodes. The electrodes that form these signals are located on the limbs—one each on the left arm (LA) and the right arm (RA) and one on the left leg (LL). The Einthoven leads form a vector triangle known as Einthoven's triangle. If any two Einthoven leads (two sides of Einthoven triangle) are known, the third one can be derived by vector addition using the closure property of Einthoven triangle.
Augmented Limb Leads: Leads aVR, aVL, and aVF are augmented limb leads. They are derived from the same three electrodes as Leads I, II, and III and involve a rotation of axes.
Chest Leads: V1, V2, V3, V4, V5, and V6 are chest leads. These represent electric potential differences between electrodes placed on various points across the chest and the Einthoven's Reference Potential (see below).
Derived Leads: Here, Lead III, aVR, aVL, and aVF are derived leads as they are being derived from Lead I and Lead II by using vector addition and axes rotation.
Einthoven's triangle: An imaginary equilateral triangle having the heart at its center and formed by lines that represent the three standard limb leads i.e leads I, II, and III. of the electrocardiogram.
Einthoven's Center Point: (ECP)—The center point of the Einthoven's triangle.
Einthoven's Reference Potential (ERP)—It is realized by taking the average of the three electric potentials at the LA, RA and LL. Einthoven's Reference Potential offers a reference point with respect to which chest leads are measured.
The ECG measuring device terminology used henceforth in this document is as follows:
Electronic Signal Channel (ESC)—The path in the electronic circuit, along which a selected ECG signal (for example, from the output of the multiplexer) flows through the various stages of analog electronic processing, is termed as an ESC. The number of different ECG voltage signals processed simultaneously is equal to the number of ESCs in the ECG device.
ECG Channel—An ECG channel refers to the display of the ECG signals, each corresponding to any one of the ECG leads. Thus, a multi-channel ECG displays a number of signals corresponding to different ECG leads simultaneously.
ECG Lead—Refers to the electric potential difference for a given configuration, e.g., the Einthoven's Leads, Augmented Limb Leads or the Chest Leads.
Leadwire—An electrocardiographic cable which physically connects one of the electrodes placed on the body surface to the ECG measuring electronic system.
Due to combination of many factors, for example, stress, dietary imbalances, irregular schedules, life style, etc., the percentage of the population suffering with heart diseases is increasing. The ratio of available doctors to number of patients, especially in the context of developing countries, is small. On the other hand, the infrastructure and medical instrumentation available is inadequate to cater to the needs of large populations in developing countries. Consequently, long queues of patients and helpless doctors trying to attend to a bit too many patients, within the limited time available to them, is a common sight. The capacity of the general masses to pay for medical tests and diagnosis is also very limited. Thus, there is a need for high throughput and low cost monitoring and diagnostic medical instruments. One such widely used medical instrument is an Electro Cardio Graph (ECG). An ECG measures the electrical activity of the heart captured over time through external skin electrodes. Moreover, whenever the symptoms of a heart disorder are felt by patients, they have to be rushed to the nearest hospital or medical practitioner, where the ECG of the patient needs to be recorded, resulting in a delay between the time of the occurrence of the event and the ECG recording. This delay must be avoided in crucial cases. A low cost and easy to use ECG device is thus desirable with a feature to transmit the recorded ECG to a hospital or doctor. For example, as many homes now have a PC, it would be natural to have a PC plug-in ECG module, whereby the recorded ECG could be easily transmitted over the internet. It would also be desirable to have a compact, light weight and portable but full featured ECG device which a general practitioner could carry in one's medical kit bag for use in emergency in field conditions. It has been further recognized that the effectiveness of ECG recording devices involves not only how well cardiac signals are measured and recorded, but also its ease of use and fast turnaround time. Many available compact palmtop ECG machines with a single ECG channel display only one Lead at a time and thus have low throughput. Several such low cost machines are further limited to measuring only one ECG-lead and thus provide limited information for medical diagnosis. Alternatively, to improve upon the throughput of the device, multi-ECG channel machines are used which in general either employ “ADC per ESC setup” or “Shared ADC setup”. The “ADC per ESC setup” uses separate circuitry (amplifiers, filters, ADC etc) in hardware for each ESC for simultaneous measurement of the ECG voltage signals. This makes the device bulky and comparatively more expensive. The power consumption of the device also increases. The maintainability of the device decreases because of the increased number of hardware components used in the design. Moreover, in the multi ESC system, there are always small gain differences from one ESC to another which further dictates that the calibration for each of the many ESCs has to be maintained for adequate accuracy of measurement. Another shortcoming of the scheme is that any drift over time in calibration of individual ESC will cause errors for the derived leads. To reduce the number of hardware components and the power consumption and to further improve upon the relative ease of calibration, maintainability and portability of the device, the second approach namely “Shared ADC setup” can be used. In this scheme ECG signals are multiplexed to allow switching of the measurement electronics from one ESC to another automatically. The use of a Shared-ADC-setup ensures that any ADC error, if any, is the same for all the ESCs.
Generally two principle setups are used in this type of multichannel digital acquisition systems namely:
Model 1) Shared ADC with separate S/H (Sample and Hold circuit).
Model 2) Shared ADC setup with shared S/H.
In the case of ECG, some leads are often derived from other leads by appropriate linear combination of two of the measured leads. For derived leads, all the directly measured ECG leads must be sampled at exactly the same instant of time, because the linear combinations of two components of a vector measured at slightly different instants of time will introduce an error in the derived leads.
One way to address the need for sampling all the ECG leads at exactly the same instant of time is to use Sample and Hold (S/H) circuits separately for each ESC while still sharing the ADC (Model 1). An analog multiplexer is used to scan the S/H outputs, and a single ADC is used to convert the ECG voltage signals sequentially to produce a serial output signal. One of the shortcomings of this scheme includes the charge leakage currents in the S/H circuits, which cause additional gain differences from one ESC to another and hence dictates the use of proper calibration to be maintained for each of the various ESC. The second alternate approach to further reduce the number of hardware components uses one single S/H circuit (usually included in an ADC electronic chip) on shared basis for the entire ESC along with the shared ADC (Model 2). An analog multiplexer is used to select the (analog) input ESC. Sequentially, each ECG voltage signal from an ESC is stored in the S/H circuit and converted to digital format by the ADC.
This scheme offers the benefit of employing lesser number of hardware components. Minimizing the number of hardware components not only helps reducing the size and cost of the device, but also helps in improving the maintainability and reliability. The power consumption of the device also reduces which is a desirable feature for the portable devices. But there are two major concerns with this scheme (Model 2):
(a) the ECG voltage signals from different ESCs are multiplexed, and hence not sampled at exactly the same instant of time. As some of the leads are derived from other leads, linear combinations of two components of a vector measured at slightly different instants of time introduce an error in the derived leads and(b) when each ECG voltage signal is switched and sampled by ADC for digitization, there is always a possibility of a spike/glitch/impulse giving an erroneous reading. Spikes contain frequency components, some of which lie within range of frequencies of the ECG pass band and can not be removed by usual FIR filters. Impulsive noise is noise of short duration, particularly of high intensity, such as that produced by turning on/off of a high mains current device in the vicinity, power fluctuations etc. Moreover, charge injection by the switching of the analog multiplexer can also impose glitches on the S/H output. Channel switching, with the associated sudden voltage change in analog circuits, may also introduce effects like overshoot, undershoot and ringing etc., which may lead to further deterioration of the sampled signal. Both the schemes used in Model 1 and Model 2 of Shared ADC setup use multiplexing and switching of signals which may introduce “Channel Switching Noise”.
In this invention, we address the first issue of errors in derived leads caused by sequential sampling of ECG leads in the “Shared ADC setup 2” by mathematical interpolation of the measured leads to the same instant of time. The second issue of spike/glitch/impulse, which have some frequency components in the ECG frequency pass band and can cause ringing effects in the measured signals once bandpass filters are applied to filter such noise, is addressed in this invention with the application of a burst sampling technique.
Reference may be made to Article: A suppression of an impulsive noise in ECG signal processing Pander, T.P. Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE. Volume 1, 1-5 Sep. 2004 Page(s):596-599. Digital Object Identifier 10.1109/IEMBS.2004.1403228.
Biomedical signals are commonly recorded with accompanying noise. Many different kinds of noise exist in the biomedical environment. One of the components of noise is a waveform due to electrical activity of the muscles. This “natural” distortion is usually modeled with a white Gaussian noise. But such assumption is not always true, because real-life muscle noise has sometimes impulsive character. First objective of this paper is an application of the alpha-stable distribution as a model of the real-life muscle noise in the ECG signal. Second objective of this paper is an application of a family of M-filters to suppression an impulsive noise in biomedical signals (ECG signals). The reference filter is the median filter. This prior art relates to filtering muscle noise from the ECG signals by using M-filters. These filters are non-linear and computationally inconvenient. Also, the reduction obtained in muscle noise is at the cost of distortion in the ECG signal of interest. In our invention muscle noise is filtered with the linear digital FIR filters.
Reference may be made to Article: Spike detection in biomedical signals using midprediction filters. S. Dandapat and G. C. Ray Med. & Biological Engg & Computing, Volume 35, Number 4, July 1997, pp. 354-360.
Summary: Sudden changes in biomedical signals, such as the QRS complex in ECG, epileptic seizures in EEG, etc. are treated as spikes in a relatively slowly varying background signal. Detection of such “spikes” with minimal distortion is the objective of this work. Such spikes, because of their inherent high frequency content, appear as an error signal in a linear prediction scheme. A symmetric midprediction filtering scheme is proposed wherein the distortion of the detected spike as well as reliability of detection is improved. This prior art relates to treating the QRS-complex as a “spike” in the ECG signal and detecting it reliably with little distortion. This “spike” is the signal of interest to be detected. On the contrary, our invention relates to elimination of undesirable sudden noise spikes (higher in frequency than the QRS complex) in the ECG signal. Our invention typically filters out spikes of frequency equal to or above the burst sampling frequency.
Reference may be made to U.S. Pat. No. 5,999,845A “Muscle Artifact Noise Detector for ECG Signal” This invention provides detection and filtering system for detecting and filtering line noise, baseline wander, and wide band noise such as muscle artifact signals to maximize the filtering of noise signals. The Detector compares noise levels to threshold values and reports the resulting noise status to cancellation filters.—It further provides the operator with the ability to manually or automatically activate the filters and to indicate the status of the filters on a printout or display. This prior art requires blanking of the QRS complex for estimation of noise. Many filter implementations are of the IIR type which in principle can be unstable. Also, individual microprocessors are required for filtering of noise in each measured channel. Filters have to be activated automatically or manually by an operator.
Our invention does not require automatic or manual activation of filters by an operator. Instead, our method uses a series of filters like stable linear digital FIR filters along with Burst Sampling Technique for removing power line interference; spike/glitch, wide band noise and baseline wander noise in the ECG signal. No separate microcontrollers/microprocessors are required for each ECG signal measured.
Reference may be made to U.S. Pat. No. 5,908,393A “Reducing noise in a biological system”: This prior art includes acquiring a biological signal, such as an ECG Signal, comparing it to a representative signal and generating a predicted signal. The predicted signal is subtracted from biological signal to produce a second signal. This second signal is passed through a filter to produce a filtered signal. The predicted signal and the filtered signals are then combined to produce noise reduced signal.
The filtering in our invention is independent of any representative or predictive signals. Reference may be made to U.S. Pat. No. 5,704,365A “Using related signals to reduce ECG Noise” The invention provides an improved technique for reducing noise in physiologic signals by using each signal as a vector projection of the underlying ECG generator onto the body surface and combines the signals in a manner that optimally reduces the noise while preserving the net vector direction of the ECG generator. In accordance with the principles of this prior art the process includes obtaining multiple input signals, measuring a relationship between noise content of the input signal, and combining the input signals in consideration of the measured relationship to produce an output signal having low noise content. This prior art relies upon measurement of secondary input signals representing noise. Noise filtering in our invention does not require measurement of any extra secondary signals representing noise. Reference may be made to U.S. Pat. No. 7,221,975B2 “Signal filtering using orthogonal polynomials and removal of edge effects”: This prior art describes method of filtering an input signal containing wanted signal components and unwanted noise components, comprising modeling the input signal as a set of polynomials, identifying polynomials from the set to model the unwanted signal components, and removing the unwanted signal components from the input signal by removing the polynomials identified as modeling the unwanted signal components from the set of polynomials to thereby leave the input signal only the wanted signal components.
In the above prior art, the input signal and the unwanted noise have to be modeled by a set of polynomials, whereas our invention uses noise filters which do not require any modeling of the input ECG signals or noise.
Reference may be made to U.S. Pat. No. 5,269,313A “Filter and method for filtering baseline wander”: The invention discloses a linear phase high pass filter using a linear phase low pass filter in parallel with an electronic delay for removing baseline wander from an ECG signal. A digital IIR filter is preferably used as the linear phase low pass filter.
The filtering methods used in above prior art depends on IIR filter which in principle can become unstable. It also employs an electronic delay and hence requires additional hardware for each measured ECG signal. Our invention employs stable linear FIR filters with no requirement of electronic delays.
Reference may be made to WO93/05574 “ECG Muscle Artifact Filter System”: The invention discloses a methodology for filtering muscle artifact signals from an ECG signal. The ECG signal is passed through a LPF having variable cut off frequencies during the portion of the ECG signal exclusive of the QRS complex. At a time slightly prior to the onset of QRS complex, the cut off frequency is incrementally increases to higher cut off frequency to pass the QRS complex with a minimum reduction of amplitude of the QRS signal. At the end of the QRS signal, the cut off frequency of filter is incrementally returned to the low cut-off frequency. An adaptive filter with low cut off frequency is used to reduce Muscle Artifact (implemented in hardware).
The above prior art employs multi-module hardware (for electronic delay, R-wave detection system, smoothing filter and adaptive bandwidth control) for each of the ECG signal measurement channels to filter muscle artifacts. Our invention does not rely upon any additional hardware for filtering muscle noise. The digital firmware/software based FIR filters remove power line, muscle artifact and baseline wandering noise. In addition, the burst sampling based filter removes impulsive and channel switching ADC noise.
A study of the prior art reveals that the existing ECG monitoring devices do not address the removal of Channel Switching ADC Noise and glitches/spikes. The classes of multiplexed ECG signal measurement devices also do not address any method for correcting the digitized ECG signal points for time delay in switching between different channels. For example, some conventional monitors filter only EMG Noise, Power Line Interference, Baseline Wandering and background noise. Some of them use IIR filters which are not necessarily linear and, in principle, can be unstable. Some others employ complex multi-module hardware to achieve filtering of various types of noise. Some of the devices simply monitor and display ECG signals and thus provide no ECG data recording capability at all. Others record ECG data and provide only for the local playback of recorded data and thus provide no remote diagnostic capability. Still other devices use bulky and expensive hardware circuitry for calibrating the device, filtering the signal using analog low pass and high pass filters etc and for clip detection. Still others simply display and record the ECG data without calculating and displaying vital heart parameters at least in the monitoring grade device.
Thus, there is a need for compact, lightweight, portable, fast turn around time, and low cost monitoring devices for recording ECG signals. A provision of a configurable set of features/set-up options while maintaining user friendliness would be desirable. The specific features in a version of the device could be fine tuned according to end use. A facility to store the ECG records along with patient information, which can later on be used for further reference by the specialist, would also be needed. It has been recognized that it would be advantageous to provide a device which improves availability of basic medical care to general public by use of low cost modern electronics, automation and IT technology for monitoring equipment. It has also been recognized that it would be desirable to provide a device which could extract and display some of the important parameters related to ECG in the monitoring grade device itself using inbuilt firmware/accompanying software.